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Artificial Intelligence Unit 5

Practice objective questions for quick revision and examination preparation. Try answering each question before revealing the answer.

📚 Artificial Intelligence
📖 Unit 5
🎯 MCQs

Acting Under Uncertainty and Probabilistic Reasoning

1

Acting under uncertainty involves:

AMaking decisions with incomplete or uncertain information
BSolving deterministic problems
CUsing only propositional logic
DPerforming adversarial search
Correct AnswerMaking decisions with incomplete or uncertain information
2

Basic probability notation includes:

AVariables, events, and probabilities
BOnly variables and events
COnly probabilities and quantifiers
DOnly quantifiers and connectives
Correct AnswerVariables, events, and probabilities
3

Inference using full joint distributions involves:

AComputing probabilities by summing over the joint distribution
BSolving constraint satisfaction problems
CPerforming adversarial search
DUsing only propositional logic
Correct AnswerComputing probabilities by summing over the joint distribution
4

Two events are independent if:

AThe occurrence of one does not affect the probability of the other
BThey always occur together
CThey are mutually exclusive
DThey are represented in a Bayesian network
Correct AnswerThe occurrence of one does not affect the probability of the other
5

Bayes’ rule is used to:

AUpdate probabilities based on new evidence
BSolve deterministic problems
CPerform adversarial search
DUse only propositional logic
Correct AnswerUpdate probabilities based on new evidence
6

The formula for Bayes’ rule is:

A P(A|B) = P(B|A) × P(A) / P(B)
B P(A|B) = P(A) + P(B)
C P(A|B) = P(A) × P(B)
D P(A|B) = P(A) − P(B)
Correct Answer P(A|B) = P(B|A) × P(A) / P(B)
7

Which of the following is true about independence in probability?

ATwo events are independent if P(A|B) = P(A)
BTwo events are independent if P(A|B) = P(B)
CTwo events are independent if P(A|B) = 0
DTwo events are independent if P(A|B) = 1
Correct AnswerTwo events are independent if P(A|B) = P(A)
8

Inference using full joint distributions is:

AComputationally expensive for large domains
BOnly applicable to deterministic problems
CUnrelated to probability
DOnly used in adversarial search
Correct AnswerComputationally expensive for large domains
9

Bayes’ rule is particularly useful in:

AUpdating beliefs based on evidence
BSolving constraint satisfaction problems
CPerforming adversarial search
DUsing only propositional logic
Correct AnswerUpdating beliefs based on evidence
10

The probability of an event A given event B is denoted by:

AP(A|B)
BP(B|A)
CP(A∩B)
DP(A∪B)
Correct AnswerP(A|B)
11

Bayesian networks are used to:

ARepresent knowledge in uncertain domains
BSolve deterministic problems
CPerform adversarial search
DUse only propositional logic
Correct AnswerRepresent knowledge in uncertain domains
12

The semantics of Bayesian networks define:

AThe conditional independence relationships between variables
BThe syntax of the network
CThe inference rules
DThe quantifiers
Correct AnswerThe conditional independence relationships between variables
13

Efficient representation of conditional distributions in Bayesian networks involves:

AUsing conditional probability tables
BSolving constraint satisfaction problems
CPerforming adversarial search
DUsing only propositional logic
Correct AnswerUsing conditional probability tables
14

Approximate inference in Bayesian networks is used when:

AExact inference is computationally expensive
BThe network is small
CThe network is deterministic
DOnly propositional logic is used
Correct AnswerExact inference is computationally expensive
15

Relational and first-order probability extend probabilistic reasoning to:

AHandle relationships and objects
BSolve deterministic problems
CPerform adversarial search
DUse only propositional logic
Correct AnswerHandle relationships and objects
16

Dempster-Shafer theory is used for:

AReasoning with uncertainty and combining evidence
BSolving deterministic problems
CPerforming adversarial search
DUsing only propositional logic
Correct AnswerReasoning with uncertainty and combining evidence
17

Which of the following is true about Bayesian networks?

AThey represent conditional dependencies between variables
BThey are only applicable to deterministic problems
CThey do not involve probability
DThey are unrelated to uncertain reasoning
Correct AnswerThey represent conditional dependencies between variables
18

Approximate inference methods in Bayesian networks include:

ASampling and variational methods
BOnly exact inference
COnly constraint propagation
DOnly adversarial search
Correct AnswerSampling and variational methods
19

Relational probability models extend probabilistic reasoning to:

AHandle relationships between objects
BSolve deterministic problems
CPerform adversarial search
DUse only propositional logic
Correct AnswerHandle relationships between objects
20

Dempster-Shafer theory is used to:

ACombine evidence from multiple sources
BSolve deterministic problems
CPerform adversarial search
DUse only propositional logic
Correct AnswerCombine evidence from multiple sources

Fill in the Blanks

21 Acting under uncertainty involves making decisions with __________ information.
Correct Answerincomplete or uncertain
22 Basic probability notation includes variables, events, and __________.
Correct Answerprobabilities
23 Inference using full joint distributions involves computing probabilities by __________ over the joint distribution.
Correct Answersumming
24 Two events are independent if the occurrence of one does not affect the __________ of the other.
Correct Answerprobability
25 Bayes’ rule is used to update probabilities based on __________.
Correct Answernew evidence
26 The formula for Bayes’ rule is P(A|B) = __________.
Correct AnswerP(B)
27 Two events are independent if P(A|B) = __________.
Correct AnswerP(A)
28 Inference using full joint distributions is computationally __________ for large domains.
Correct Answerexpensive
29 Bayes’ rule is particularly useful in updating __________ based on evidence.
Correct Answerbeliefs
30 The probability of an event A given event B is denoted by __________.
Correct AnswerP(A|B)
31 Bayesian networks are used to represent knowledge in __________ domains.
Correct Answeruncertain
32 The semantics of Bayesian networks define the __________ relationships between variables.
Correct Answerconditional independence
33 Efficient representation of conditional distributions in Bayesian networks involves using __________.
Correct Answerconditional probability tables
34 Approximate inference in Bayesian networks is used when exact inference is computationally __________.
Correct Answerexpensive
35 Relational and first-order probability extend probabilistic reasoning to handle __________ and objects.
Correct Answerrelationships
36 Dempster-Shafer theory is used for reasoning with __________ and combining evidence.
Correct Answeruncertainty
37 Bayesian networks represent __________ dependencies between variables.
Correct Answerconditional
38 Approximate inference methods in Bayesian networks include __________ and variational methods.
Correct Answersampling
39 Relational probability models extend probabilistic reasoning to handle __________ between objects.
Correct Answerrelationships
40 Dempster-Shafer theory is used to combine __________ from multiple sources.
Correct Answerevidence
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